zkML: Verifiable AI. In maths we trust.
Generate machine learning inferences with zero-knowledge proof of execution. Deliver verifiable AI outputs.What is zkML?
zkML (Zero-Knowledge Machine Learning) is the next evolution of AI: it allows anyone to verify that an AI model was executed correctly—without revealing how the output was generated or what data it was based on.
This is made possible through zero-knowledge proofs (ZKPs)—a form of cryptographic assurance that lets a system prove the truth of a computation, without exposing the inputs or internal processes.
With zkML, you can:
Prove your model’s results without sharing model details.
Give AI agents an identity with verifiable integrity.
Build trust into AI workflows—by default.
How It Works
zkML bridges machine learning models and zero-knowledge cryptography using an optimized compiler pipeline.
1. Build Your Model (PyTorch-native)
Write and train your model in PyTorch—no major code changes needed. zkML supports standard layers out of the box. Complex or custom ops may require adaptation.
2. Compile to Zero-Knowledge Circuits
The model is transformed into a ZK-friendly format using:
- Graph-based preprocessing (via ONNX)
- ZKP-friendly quantization
- Hierarchical circuit optimization
3. Generate a Proof of Correctness
At inference time, the model runs on private inputs and produces:
- The output (e.g., classification or prediction)
- A zero-knowledge proof that the output is correct for the specific model and inputs
Note: Proof generation times vary by model and hardware. For example: ~2.2s for VGG-16 (15M parameters) on CIFAR-10 and ~150s per token for Llama-3 (8B parameters) on a single CPU core.
Why zkML?
Verifiable Trust
End users, auditors, or partners can verify the model’s outputs were computed correctly without any trust assumptions.
Fast & Scalable
Supports modern architectures like CNNs and Transformers. Optimized to generate zero-knowledge proofs in seconds (e.g., 2.2s for VGG-16 on CIFAR-10, 150s per token on Llama-3 scale models).
AI-Native Integration
No need to rewrite models. zkML works directly with PyTorch models and integrates into ML workflows with minimal overhead.
Use Cases
AI Agents
Assign an identity to a trusted AI agent, allowing it to safely perform critical tasks. Verify genuine, tamper-proof results.ML-as-a-Service
Offer predictions with verifiable correctness. Keep proprietary models secure while building user trust.Healthcare & Finance
Enable secure, compliant AI services where sensitive inputs and models must remain confidential.Compliance & Auditing
Prove that AI systems don’t use sensitive attributes (like race or gender) in decisions—without exposing the model logic.Built for the Future
zkML enables a new standard of AI accountability. With it, organizations can deliver powerful machine learning solutions that are not only accurate—but also provable and trustworthy.
Your model is the secret.
Your proof is the trustless guarantee.
With zkML, AI and trust go hand in hand.
Frequently Asked Questions
Developer Ready
Polyhedra offers:
SDKs for Python & Rust
REST APIs for proof generation & verification
Native support for Expander zk-prover
Full documentation & example notebooks
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